Files
WhisperLiveKit/whisperlivekit/thread_safety.py
2026-01-09 11:23:19 -05:00

140 lines
3.8 KiB
Python

"""
Thread Safety Configuration for WhisperLiveKit
This module provides thread safety configuration and utilities.
Environment Variables:
WHISPERLIVEKIT_MODEL_LOCK: Enable/disable model locking (default: 1)
Set to "0" to disable for single-connection deployments
WHISPERLIVEKIT_LOCK_TIMEOUT: Lock acquisition timeout in seconds (default: 30)
Usage:
# Enable model locking (default)
export WHISPERLIVEKIT_MODEL_LOCK=1
# Disable for single-connection deployment
export WHISPERLIVEKIT_MODEL_LOCK=0
# Custom timeout
export WHISPERLIVEKIT_LOCK_TIMEOUT=60
"""
import os
import logging
import threading
logger = logging.getLogger(__name__)
# Configuration
USE_MODEL_LOCK = os.environ.get("WHISPERLIVEKIT_MODEL_LOCK", "1") == "1"
LOCK_TIMEOUT = float(os.environ.get("WHISPERLIVEKIT_LOCK_TIMEOUT", "30.0"))
# Global model lock
_model_lock = threading.Lock()
# Log configuration on import
if USE_MODEL_LOCK:
logger.info(f"Model locking ENABLED (timeout: {LOCK_TIMEOUT}s)")
logger.info("For single-connection deployments, set WHISPERLIVEKIT_MODEL_LOCK=0")
else:
logger.warning("Model locking DISABLED - only safe for single-connection deployments")
def get_model_lock():
"""Get the global model lock instance"""
return _model_lock
def acquire_model_lock(timeout=None):
"""
Acquire model lock with timeout.
Args:
timeout: Lock acquisition timeout (default: use LOCK_TIMEOUT)
Returns:
bool: True if lock acquired, False on timeout
"""
if not USE_MODEL_LOCK:
return True
timeout = timeout or LOCK_TIMEOUT
acquired = _model_lock.acquire(timeout=timeout)
if not acquired:
logger.error(f"Failed to acquire model lock within {timeout}s")
return acquired
def release_model_lock():
"""Release model lock"""
if not USE_MODEL_LOCK:
return
try:
_model_lock.release()
except RuntimeError:
# Lock not held - this is fine
pass
class ModelLockContext:
"""Context manager for model lock"""
def __init__(self, timeout=None):
self.timeout = timeout
self.acquired = False
def __enter__(self):
self.acquired = acquire_model_lock(self.timeout)
return self.acquired
def __exit__(self, exc_type, exc_val, exc_tb):
if self.acquired:
release_model_lock()
return False
# Concurrency recommendations
RECOMMENDED_CONNECTIONS_PER_WORKER = 1 if USE_MODEL_LOCK else 1
RECOMMENDED_WORKERS = 4
def print_deployment_recommendations():
"""Print recommended deployment configuration"""
print("\n" + "="*60)
print("WhisperLiveKit Deployment Recommendations")
print("="*60)
if USE_MODEL_LOCK:
print("⚠️ Model locking is ENABLED")
print(" This serializes inference across connections.")
print()
print("Recommended deployment:")
print(f" gunicorn -w {RECOMMENDED_WORKERS} \\")
print(" -k uvicorn.workers.UvicornWorker \\")
print(" --worker-connections 1 \\")
print(" whisperlivekit.basic_server:app")
print()
print("Expected capacity:")
print(f" - {RECOMMENDED_WORKERS} concurrent users (1 per worker)")
print(f" - Memory: ~{RECOMMENDED_WORKERS}x model size")
else:
print("✅ Model locking is DISABLED")
print(" ⚠️ ONLY safe for single-connection deployments")
print()
print("Recommended deployment:")
print(" uvicorn whisperlivekit.basic_server:app \\")
print(" --host 0.0.0.0 --port 8000 \\")
print(" --workers 1")
print()
print("Expected capacity:")
print(" - 1 concurrent user only")
print("="*60 + "\n")
if __name__ == "__main__":
print_deployment_recommendations()